{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Audio t-SNE\n",
    "\n",
    "This notebook will show you how to create a t-SNE plot of a group of audio clips. Along the way, we'll cover a few basic audio processing and machine learning tasks.\n",
    "\n",
    "We will make two separate t-SNE plots. The first is clustering a group of many audio files in a single directory. The second one takes only a single audio track (a song) as it's input, segments it into many audio chunks (and saves them to a directory) and clusters the resulting chunks.\n",
    "\n",
    "This notebook requires numpy, matplotlib, scikit-learn, and [librosa](https://github.com/librosa/librosa) to run. Refer to those libraries themselves for installation instructions.\n",
    "\n",
    "After verifying you have the required libraries, verify the following import commands work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import fnmatch\n",
    "import os\n",
    "import numpy as np\n",
    "import librosa\n",
    "import matplotlib.pyplot as plt\n",
    "import librosa.display\n",
    "from sklearn.manifold import TSNE\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to scan some directory of audio files and collect all their paths into a single list. This notebook is using a free sample pack called \"Vintage drum machines\" which can be downloaded, along with all the other data needed for ml4a-guides, by running the script `download.sh` in the `data` folder, or downloaded and unzipped manually from [http://ivcloud.de/index.php/s/QyDXk1EDYDTVYkF](http://ivcloud.de/index.php/s/QyDXk1EDYDTVYkF).\n",
    "\n",
    "Once you've done that, or changed the `path` variable to another directory of audio samples on your computer, you can proceed with the next code block to load all the filepaths into memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found 6153 .wav files in ../data/Vintage Drum Machines\n"
     ]
    }
   ],
   "source": [
    "path = '../data/Vintage Drum Machines'\n",
    "\n",
    "files = []\n",
    "for root, dirnames, filenames in os.walk(path):\n",
    "    for filename in fnmatch.filter(filenames, '*.wav'):\n",
    "        files.append(os.path.join(root, filename))\n",
    "\n",
    "print(\"found %d .wav files in %s\"%(len(files),path))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next block, we're going to create a function which extracts a feature vector from an audio file. We are using [librosa](https://github.com/librosa/librosa/), a python library for audio analysis and music information retrieval, to handle the feature extraction.\n",
    "\n",
    "The function we're creating `get_features` will take a waveform `y` at a sample rate `sr`, and extract features for only the first second of the audio. It is possible to use longer samples, but because we are interested in clustering according to sonic similarity, we choose to focus on short samples with relatively homogenous content over their durations. Longer samples have different sections and would require somewhat more sophisticated feature extraction.\n",
    "\n",
    "The feature extraction will calculate the first 13 [mel-frequency cepstral coefficients](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum) of the audio file, as well as their first- and second-order derivatives, and concatenate them into a single 39-element feature vector. The feature vector is also [standardized](https://en.wikipedia.org/wiki/Standard_score) so that each feature has equal variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_features(y, sr):\n",
    "    y = y[0:sr]  # analyze just first second\n",
    "    S = librosa.feature.melspectrogram(y, sr=sr, n_mels=128)\n",
    "    log_S = librosa.logamplitude(S, ref_power=np.max)\n",
    "    mfcc = librosa.feature.mfcc(S=log_S, n_mfcc=13)\n",
    "    delta_mfcc = librosa.feature.delta(mfcc)\n",
    "    delta2_mfcc = librosa.feature.delta(mfcc, order=2)\n",
    "    feature_vector = np.concatenate((np.mean(mfcc,1), np.mean(delta_mfcc,1), np.mean(delta2_mfcc,1)))\n",
    "    feature_vector = (feature_vector-np.mean(feature_vector))/np.std(feature_vector)\n",
    "    return feature_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will iterate through all the files, and get their feature vectors, placing them into a new list `feature_vectors`. We also make a new array `sound_paths` to index the feature vectors to the correct paths, in case some of the files are empty or corrupted (as they are in the `Vintage Drum Machines` sample pack), or otherwise something goes wrong in analyzing them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "get 1 of 6153 = ../data/Vintage Drum Machines/Ace Tone Rhythm Ace/CLAVE.wav\n",
      "get 101 of 6153 = ../data/Vintage Drum Machines/Akai  XR-10/Snaredrums/Snaredrum-09.wav\n",
      "get 201 of 6153 = ../data/Vintage Drum Machines/Akai XR10/XR10Rimshot01.wav\n",
      "get 301 of 6153 = ../data/Vintage Drum Machines/Alesis DM5/DM5Hat_C07.wav\n",
      "get 401 of 6153 = ../data/Vintage Drum Machines/Alesis DM5/DM5Kick89.wav\n",
      "get 501 of 6153 = ../data/Vintage Drum Machines/Alesis DM5/DM5Perc094.wav\n",
      "get 601 of 6153 = ../data/Vintage Drum Machines/Alesis DM5/DM5Snare072.wav\n",
      "get 701 of 6153 = ../data/Vintage Drum Machines/Alesis HR-16/Agogo Bell.wav\n",
      "get 801 of 6153 = ../data/Vintage Drum Machines/Alesis HR16B/HR16B 038 Bottles.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_hats22/SR16 Hat_Closed.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_hats22/SR16 Hat_Small.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_hats22/SR16 Hat_Tight.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_hats22/SR16 Hat_Vari.wav\n",
      "get 901 of 6153 = ../data/Vintage Drum Machines/Alesis SR15/sr16_kik22/SR16 Kik_Dry Wood 2.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_kik22/SR16 Kik_Dry05.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_kik22/SR16 Kik_Dry08.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_kik22/SR16 Kik_Elect Hi.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_perc22/SR16 Prc_Agogo Hi.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_perc22/SR16 Prc_Cabasa.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_perc22/SR16 Prc_Clave Hi.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_perc22/SR16 Prc_Clave Lo.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_perc22/SR16 Prc_Cowbell Hi.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_perc22/SR16 Prc_Fish Lo.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_perc22/SR16 Prc_Fishstick.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_perc22/SR16 Prc_Shaker.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_perc22/SR16 Prc_Sticks Hi.wav\n",
      "get 1001 of 6153 = ../data/Vintage Drum Machines/Alesis SR15/sr16_snares/SR16 Snr_Flange.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_toms/SR16 Tom_Elect 1.wav\n",
      "error loading ../data/Vintage Drum Machines/Alesis SR15/sr16_toms/SR16 Tom_Elect 2.wav\n",
      "get 1101 of 6153 = ../data/Vintage Drum Machines/Alesis SR16/SR16Hat_O2.wav\n",
      "get 1201 of 6153 = ../data/Vintage Drum Machines/Boss DR-202/202kik05.wav\n",
      "get 1301 of 6153 = ../data/Vintage Drum Machines/Boss DR-202/202snr05.wav\n",
      "get 1401 of 6153 = ../data/Vintage Drum Machines/Boss DR-55/Rimshot.wav\n",
      "get 1501 of 6153 = ../data/Vintage Drum Machines/Boss DR-550/Toms/Tom M-02.wav\n",
      "get 1601 of 6153 = ../data/Vintage Drum Machines/Boss DR-660/DR-660Perc36.wav\n",
      "get 1701 of 6153 = ../data/Vintage Drum Machines/Boss DR-660/DR-660Snare61.wav\n",
      "get 1801 of 6153 = ../data/Vintage Drum Machines/Cheetah MD16/CHTMD38.wav\n",
      "get 1901 of 6153 = ../data/Vintage Drum Machines/Emu SP12/SNARES/Rim Shot-01.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_00.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_01.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_02.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_03.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_04.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_05.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_06.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_07.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_08.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_09.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_10.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_11.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_12.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_13.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_14.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_15.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_16.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_17.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_18.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_19.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_20.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_21.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_22.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_23.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_24.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_25.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_26.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_27.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_28.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_29.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_30.wav\n",
      "error loading ../data/Vintage Drum Machines/Kawai K3/K3M_31.wav\n",
      "get 2001 of 6153 = ../data/Vintage Drum Machines/Kawai R50/R50 Timbale.wav\n",
      "get 2101 of 6153 = ../data/Vintage Drum Machines/Korg KPR77/KPRTom_Lo.wav\n",
      "get 2201 of 6153 = ../data/Vintage Drum Machines/Korg Poly-800/Bassdrum-02.wav\n",
      "error loading ../data/Vintage Drum Machines/Korg T3/Rim shot.wav\n",
      "get 2301 of 6153 = ../data/Vintage Drum Machines/Linn LinnDrum/Percussion/Conga L-02.wav\n",
      "get 2401 of 6153 = ../data/Vintage Drum Machines/Novation Drumstation/Kit00/DS00808TomLo.wav\n",
      "get 2501 of 6153 = ../data/Vintage Drum Machines/Novation Drumstation/Kit04/DS04808Cymbal.wav\n",
      "get 2601 of 6153 = ../data/Vintage Drum Machines/Novation Drumstation/Kit07/DS07909TomLo.wav\n",
      "get 2701 of 6153 = ../data/Vintage Drum Machines/Novation Drumstation/Kit11/DS11909Crash.wav\n",
      "get 2801 of 6153 = ../data/Vintage Drum Machines/Novation Drumstation/Kit15/DS15808Kick.wav\n",
      "get 2901 of 6153 = ../data/Vintage Drum Machines/Novation Drumstation/Kit19/DS19808Clave.wav\n",
      "get 3001 of 6153 = ../data/Vintage Drum Machines/Novation Drumstation/Kit22/DS22909Kick.wav\n",
      "get 3101 of 6153 = ../data/Vintage Drum Machines/Quasimidi 309/QuasiA 043.wav\n",
      "get 3201 of 6153 = ../data/Vintage Drum Machines/Quasimidi 309/QuasiB 093.wav\n",
      "get 3301 of 6153 = ../data/Vintage Drum Machines/Rhythm 33/rhythm33_tom.wav\n",
      "get 3401 of 6153 = ../data/Vintage Drum Machines/Roland CR-8000/CR8000Tom_Lo.wav\n",
      "get 3501 of 6153 = ../data/Vintage Drum Machines/Roland JD-990/Org_bell.wav\n",
      "get 3601 of 6153 = ../data/Vintage Drum Machines/Roland JD800 Dance Card/JD800 Dance Card Drums 030.wav\n",
      "get 3701 of 6153 = ../data/Vintage Drum Machines/Roland MC-303/Percussion/Hibamboo.wav\n",
      "get 3801 of 6153 = ../data/Vintage Drum Machines/Roland MC09/MC09 HatO_02.wav\n",
      "get 3901 of 6153 = ../data/Vintage Drum Machines/Roland R-5/R-5 Reverb Snare.wav\n",
      "get 4001 of 6153 = ../data/Vintage Drum Machines/Roland R-8/R8Kick06.wav\n",
      "get 4101 of 6153 = ../data/Vintage Drum Machines/Roland R-8/R8Snare46.wav\n",
      "get 4201 of 6153 = ../data/Vintage Drum Machines/Roland R8/SNARES/Snaredrum-07.wav\n",
      "get 4301 of 6153 = ../data/Vintage Drum Machines/Roland SH-09/Bassdrum-40.wav\n",
      "error loading ../data/Vintage Drum Machines/Roland TR-505/Bassdrum.wav\n",
      "error loading ../data/Vintage Drum Machines/Roland TR-505/Clap.wav\n",
      "error loading ../data/Vintage Drum Machines/Roland TR-505/Conga H.wav\n",
      "error loading ../data/Vintage Drum Machines/Roland TR-505/Cowbell H.wav\n",
      "error loading ../data/Vintage Drum Machines/Roland TR-505/Cowbell L.wav\n",
      "error loading ../data/Vintage Drum Machines/Roland TR-505/Hat Closed.wav\n",
      "error loading ../data/Vintage Drum Machines/Roland TR-505/Rimshot.wav\n",
      "error loading ../data/Vintage Drum Machines/Roland TR-505/Snaredrum.wav\n",
      "get 4401 of 6153 = ../data/Vintage Drum Machines/Roland TR-606/Hat Closed.wav\n",
      "get 4501 of 6153 = ../data/Vintage Drum Machines/Roland TR-626/TR-626Shaker.wav\n",
      "get 4601 of 6153 = ../data/Vintage Drum Machines/Roland TR-808/TR-808Hat_C05.wav\n",
      "get 4701 of 6153 = ../data/Vintage Drum Machines/Roland TR-909/TR-909Snare 10.wav\n",
      "get 4801 of 6153 = ../data/Vintage Drum Machines/Sequential Drumtrax/DrumtraxKick.wav\n",
      "get 4901 of 6153 = ../data/Vintage Drum Machines/Simmons SDS-5/BASSDRUM/Bassdrum-06.wav\n",
      "get 5001 of 6153 = ../data/Vintage Drum Machines/Simmons SDSV/sds5_misc/SDS5 06.wav\n",
      "get 5101 of 6153 = ../data/Vintage Drum Machines/X Drum LM8953/Tom-02.wav\n",
      "get 5201 of 6153 = ../data/Vintage Drum Machines/Yamaha CS6/Yamaha CS6 B 004.wav\n",
      "get 5301 of 6153 = ../data/Vintage Drum Machines/Yamaha EX5/EX5B 008.wav\n",
      "get 5401 of 6153 = ../data/Vintage Drum Machines/Yamaha RM 50/Bassdrums/BD-012.wav\n",
      "get 5501 of 6153 = ../data/Vintage Drum Machines/Yamaha RM 50/Cymbals/CYMBAL_009.wav\n",
      "get 5601 of 6153 = ../data/Vintage Drum Machines/Yamaha RM 50/FX/FX_049.wav\n",
      "error loading ../data/Vintage Drum Machines/Yamaha RM 50/FX/FX_136.wav\n",
      "error loading ../data/Vintage Drum Machines/Yamaha RM 50/FX/FX_137.wav\n",
      "error loading ../data/Vintage Drum Machines/Yamaha RM 50/FX/FX_138.wav\n",
      "get 5701 of 6153 = ../data/Vintage Drum Machines/Yamaha RM 50/Snaredrums/SNAREDRUM_040.wav\n",
      "get 5801 of 6153 = ../data/Vintage Drum Machines/Yamaha RM 50/Toms/TOMS_032.wav\n",
      "get 5901 of 6153 = ../data/Vintage Drum Machines/Yamaha RX11/RX11Crash.wav\n",
      "get 6001 of 6153 = ../data/Vintage Drum Machines/Yamaha RY-30/Percussion/Cabasa-02.wav\n",
      "get 6101 of 6153 = ../data/Vintage Drum Machines/Yamaha RY30/RY30Snare20.wav\n",
      "calculated 6091 feature vectors\n"
     ]
    }
   ],
   "source": [
    "feature_vectors = []\n",
    "sound_paths = []\n",
    "for i,f in enumerate(files):\n",
    "    if i % 100 == 0:\n",
    "        print \"get %d of %d = %s\"%(i+1, len(files), f)\n",
    "    try:\n",
    "        y, sr = librosa.load(f)\n",
    "        if len(y) < 2:\n",
    "            print(\"error loading %s\" % f)\n",
    "            continue\n",
    "        feat = get_features(y, sr)\n",
    "        feature_vectors.append(feat)\n",
    "        sound_paths.append(f)\n",
    "    except:\n",
    "        print(\"error loading %s\" % f)\n",
    "        \n",
    "print(\"calculated %d feature vectors\"%len(feature_vectors))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can run t-SNE over the feature vectors to get a 2-dimensional embedding of our audio files. We use scikit-learn's TSNE function, and additionally normalize the results so that they are between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing pairwise distances...\n",
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 6091\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 6091\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 6091\n",
      "[t-SNE] Computed conditional probabilities for sample 4000 / 6091\n",
      "[t-SNE] Computed conditional probabilities for sample 5000 / 6091\n",
      "[t-SNE] Computed conditional probabilities for sample 6000 / 6091\n",
      "[t-SNE] Computed conditional probabilities for sample 6091 / 6091\n",
      "[t-SNE] Mean sigma: 0.121914\n",
      "[t-SNE] Iteration 25: error = 1.7862769, gradient norm = 0.0000158\n",
      "[t-SNE] Iteration 25: gradient norm 0.000016. Finished.\n",
      "[t-SNE] Iteration 50: error = 1.7862240, gradient norm = 0.0006299\n",
      "[t-SNE] Iteration 50: gradient norm 0.000630. Finished.\n",
      "[t-SNE] KL divergence after 50 iterations with early exaggeration: 1.786224\n",
      "[t-SNE] Iteration 75: error = 1.7804098, gradient norm = 0.0110668\n",
      "[t-SNE] Iteration 100: error = 1.7665591, gradient norm = 0.0087650\n",
      "[t-SNE] Iteration 125: error = 1.7619910, gradient norm = 0.0082601\n",
      "[t-SNE] Iteration 150: error = 1.7607527, gradient norm = 0.0081405\n",
      "[t-SNE] Iteration 175: error = 1.7604173, gradient norm = 0.0081076\n",
      "[t-SNE] Iteration 200: error = 1.7603196, gradient norm = 0.0080997\n",
      "[t-SNE] Iteration 225: error = 1.7602888, gradient norm = 0.0080982\n",
      "[t-SNE] Iteration 250: error = 1.7602814, gradient norm = 0.0080974\n",
      "[t-SNE] Iteration 275: error = 1.7602798, gradient norm = 0.0080971\n",
      "[t-SNE] Iteration 300: error = 1.7602792, gradient norm = 0.0080971\n",
      "[t-SNE] Iteration 300: error difference 0.000000. Finished.\n",
      "[t-SNE] Error after 300 iterations: 1.786224\n"
     ]
    }
   ],
   "source": [
    "model = TSNE(n_components=2, learning_rate=150, perplexity=30, verbose=2, angle=0.1).fit_transform(feature_vectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot our t-SNE points.  We can use matplotlib to quickly scatter them and see their distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ho4siKqkErN0NWpeRugkQRf0uwGFIizEVvavVJhEEdRw/fsIT/bLXQdd7LTvr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\n3dJcnlfmwOdEYLedpqyL4xMd2OriuYjDuDIfFqzFWC4y0qTFhppej2A8j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S1Fv19WlhMu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\nNhhgdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106159fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_axis=model[:,0]\n",
    "y_axis=model[:,1]\n",
    "\n",
    "plt.figure(figsize = (10,10))\n",
    "plt.scatter(x_axis, y_axis)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see our t-SNE plot of our audio files, but it's not particularly interesting! Since we are dealing with audio files, there's no easy way to compare neighboring audio samples to each other. We can use some other, more interactive environment to view the results of the t-SNE. One way we can do this is by saving the results to a JSON file which stores the filepaths and t-SNE assignments of all the audio files. We can then load this JSON file in another environment. \n",
    "\n",
    "One example of this is provided in the \"AudioTSNEViewer\" application in [ml4a-ofx](https://github.com/ml4a/ml4a-ofx/tree/master/apps). This is an [openFrameworks](http://openframeworks.cc) application which loads all the audio clips into an interactive 2d grid (using the t-SNE layout) and lets you play each sample by hovering your mouse over it.  \n",
    "\n",
    "In any case, to save the t-SNE to a JSON file, we first normalize the coordinates to between 0 and 1 and save them, along with the full filepaths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved ../data/example-audio-tSNE.json to disk!\n"
     ]
    }
   ],
   "source": [
    "tsne_path = \"../data/example-audio-tSNE.json\"\n",
    "\n",
    "x_norm = (x_axis - np.min(x_axis)) / (np.max(x_axis) - np.min(x_axis))\n",
    "y_norm = (y_axis - np.min(y_axis)) / (np.max(y_axis) - np.min(y_axis))\n",
    "\n",
    "data = [{\"path\":os.path.abspath(f), \"point\":[x, y]} for f, x, y in zip(sound_paths, x_norm, y_norm)]\n",
    "with open(tsne_path, 'w') as outfile:\n",
    "    json.dump(data, outfile)\n",
    "\n",
    "print(\"saved %s to disk!\" % tsne_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now what about if we want to do this same analysis, but instead of analyzing a directory of individual audio clips, we want to cut up a _single audio file_ into many chunks and cluster those instead. \n",
    "\n",
    "We can add a few extra lines of code to the above in order to do this. First let's select a piece of audio. Find any song on your computer that you want to do this analysis to and set a path to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "source_audio = '/Users/gene/Downloads/bohemianrhapsody.mp3'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we will now do is use librosa to calculate the _onsets_ of our audio file. Onsets are the timestamps to the beginning of discrete sonic events in our audio. We set the hop_length (number of samples for each frame) to 512. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hop_length = 512\n",
    "y, sr = librosa.load(source_audio)\n",
    "onsets = librosa.onset.onset_detect(y=y, sr=sr, hop_length=hop_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we interpret these numbers?  Initially our original audio, at a sample rate `sr` is divided up into bins which each cointain 512 samples (specified by `hop_length` in the onset detection function). The onset numbers are an index to each of these bins. So for example, if the first onset is 20, this corresponds to sample 20 * 512 = 10,240. Given a sample rate of 22050, this corresponds to 10250/22050 = 0.46 seconds into the track.\n",
    "\n",
    "We can view the onsets as vertical lines on top of the waveform using matplotlib and the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x105dbc990>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SsHBhsMdv3uIvEPPbYjx3rn4fHH+898e0aePvGPmqVsC0adnr583zvg+3527j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\nuExsAGQP1O0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105eb33d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = [hop_length * onset / sr for onset in onsets]\n",
    "\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.subplot(1, 1, 1)\n",
    "librosa.display.waveplot(y, sr=sr)\n",
    "plt.vlines(times, -1, 1, color='r', alpha=0.9, label='Onsets')\n",
    "plt.title('Wavefile with %d onsets plotted' % len(times))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now what we will do is, we will go through each of the detected onsets, and crop the original audio to the interval from that onset until the next one. We will create a new folder, `../data/audio_clips`, into which we will then save all of the individual audio clips, and extract the same feature vector we described above for our new audio sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "analyzed 1/784 = /Users/gene/Downloads/bohemian_segments//onset_0.wav\n",
      "analyzed 51/784 = /Users/gene/Downloads/bohemian_segments//onset_50.wav\n",
      "analyzed 101/784 = /Users/gene/Downloads/bohemian_segments//onset_100.wav\n",
      "analyzed 151/784 = /Users/gene/Downloads/bohemian_segments//onset_150.wav\n",
      "analyzed 201/784 = /Users/gene/Downloads/bohemian_segments//onset_200.wav\n",
      "analyzed 251/784 = /Users/gene/Downloads/bohemian_segments//onset_250.wav\n",
      "analyzed 301/784 = /Users/gene/Downloads/bohemian_segments//onset_300.wav\n",
      "analyzed 351/784 = /Users/gene/Downloads/bohemian_segments//onset_350.wav\n",
      "analyzed 401/784 = /Users/gene/Downloads/bohemian_segments//onset_400.wav\n",
      "analyzed 451/784 = /Users/gene/Downloads/bohemian_segments//onset_450.wav\n",
      "analyzed 501/784 = /Users/gene/Downloads/bohemian_segments//onset_500.wav\n",
      "analyzed 551/784 = /Users/gene/Downloads/bohemian_segments//onset_550.wav\n",
      "analyzed 601/784 = /Users/gene/Downloads/bohemian_segments//onset_600.wav\n",
      "analyzed 651/784 = /Users/gene/Downloads/bohemian_segments//onset_650.wav\n",
      "analyzed 701/784 = /Users/gene/Downloads/bohemian_segments//onset_700.wav\n",
      "analyzed 751/784 = /Users/gene/Downloads/bohemian_segments//onset_750.wav\n"
     ]
    }
   ],
   "source": [
    "# where to save our new clips to\n",
    "path_save_intervals = \"/Users/gene/Downloads/bohemian_segments/\"\n",
    "\n",
    "# make new directory to save them \n",
    "if not os.path.isdir(path_save_intervals):\n",
    "    os.mkdir(path_save_intervals)\n",
    "\n",
    "# grab each interval, extract a feature vector, and save the new clip to our above path\n",
    "feature_vectors = []\n",
    "for i in range(len(onsets)-1):\n",
    "    idx_y1 = onsets[i  ] * hop_length  # first sample of the interval\n",
    "    idx_y2 = onsets[i+1] * hop_length  # last sample of the interval\n",
    "    y_interval = y[idx_y1:idx_y2]\n",
    "    features = get_features(y_interval, sr)   # get feature vector for the audio clip between y1 and y2\n",
    "    file_path = '%s/onset_%d.wav' % (path_save_intervals, i)   # where to save our new audio clip\n",
    "    feature_vectors.append({\"file\":file_path, \"features\":features})   # append to a feature vector\n",
    "    librosa.output.write_wav(file_path, y_interval, sr)   # save to disk\n",
    "    if i % 50 == 0:\n",
    "        print \"analyzed %d/%d = %s\"%(i+1, len(onsets)-1, file_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing pairwise distances...\n",
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Computed conditional probabilities for sample 784 / 784\n",
      "[t-SNE] Mean sigma: 0.231958\n",
      "[t-SNE] Iteration 25: error = 2.3879926, gradient norm = 0.0265828\n",
      "[t-SNE] Iteration 50: error = 1.9338583, gradient norm = 0.0146562\n",
      "[t-SNE] Iteration 75: error = 1.2881635, gradient norm = 0.0044903\n",
      "[t-SNE] Iteration 100: error = 1.2138703, gradient norm = 0.0037113\n",
      "[t-SNE] KL divergence after 100 iterations with early exaggeration: 1.213870\n",
      "[t-SNE] Iteration 125: error = 1.1115109, gradient norm = 0.0026617\n",
      "[t-SNE] Iteration 150: error = 1.0837917, gradient norm = 0.0024060\n",
      "[t-SNE] Iteration 175: error = 1.0771828, gradient norm = 0.0023484\n",
      "[t-SNE] Iteration 200: error = 1.0754088, gradient norm = 0.0023334\n",
      "[t-SNE] Iteration 225: error = 1.0749190, gradient norm = 0.0023294\n",
      "[t-SNE] Iteration 250: error = 1.0747885, gradient norm = 0.0023282\n",
      "[t-SNE] Iteration 275: error = 1.0747490, gradient norm = 0.0023279\n",
      "[t-SNE] Iteration 300: error = 1.0747377, gradient norm = 0.0023278\n",
      "[t-SNE] Iteration 300: error difference 0.000000. Finished.\n",
      "[t-SNE] Error after 300 iterations: 1.213870\n",
      "saved ../data/example-audio-tSNE-onsets.json to disk!\n"
     ]
    }
   ],
   "source": [
    "# save results to this json file\n",
    "tsne_path = \"../data/example-audio-tSNE-onsets.json\"\n",
    "\n",
    "# feature_vectors has both the features and file paths in it. let's pull out just the feature vectors\n",
    "features_matrix = [f[\"features\"] for f in feature_vectors]\n",
    "\n",
    "# calculate a t-SNE and normalize it\n",
    "model = TSNE(n_components=2, learning_rate=150, perplexity=30, verbose=2, angle=0.1).fit_transform(features_matrix)\n",
    "x_axis, y_axis = model[:,0], model[:,1] # normalize t-SNE\n",
    "x_norm = (x_axis - np.min(x_axis)) / (np.max(x_axis) - np.min(x_axis))\n",
    "y_norm = (y_axis - np.min(y_axis)) / (np.max(y_axis) - np.min(y_axis))\n",
    "\n",
    "data = [{\"path\":os.path.abspath(f['file']), \"point\":[x, y]} for f, x, y in zip(feature_vectors, x_norm, y_norm)]\n",
    "with open(tsne_path, 'w') as outfile:\n",
    "    json.dump(data, outfile)\n",
    "\n",
    "print(\"saved %s to disk!\" % tsne_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the results on another scatter plot. One nice thing we can do is color the points according to their order in the original track. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XpSeiKkJcxcgQWS3zmP/6edjwPMTD4AvBpEXwiQe9Zu324lF49oew/W3IKlTsXONtUxLs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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x101582990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = cm.rainbow(np.linspace(0, 1, len(x_axis)))\n",
    "plt.figure(figsize = (8,6))\n",
    "plt.scatter(x_axis, y_axis, color=colors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not surprisingly, audio clips which appear close to each other (and therefore have a similar color) also cluster together in the t-SNE. Two clips that are right next to each other probably have similar sound content and therefore similar feature vectors. But we also see different sections (e.g. teal and orange) appearing clustered together as well. This suggests those two sections may have similar audio content as well."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
